Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add amplitude/mcp-marketplace --skill analyze-experimentgit clone --depth 1 https://github.com/amplitude/mcp-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amplitude/mcp-marketplace/analyze-experiment)<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/analyze-experiment"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-experiment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/amplitude/mcp-marketplace/analyze-experiment"><img src="https://agentmods.dev/badge/skills/amplitude/mcp-marketplace/analyze-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.05038 |
| Opus 5 | $0.00024 | $0.02519 |
| Sonnet 5 | $0.00010 | $0.01008 |
| Haiku 4.5 | $0.00005 | $0.00504 |
Grade A, and why
analyze-experiments scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to analyze-experiment-consolidated — 45 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 528 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Analyst
Perform comprehensive, detailed deep-dive analysis of experiments to make data-driven ship/no-ship decisions. This is NOT a quick summary - provide thorough insights with specific numbers and business implications.
When to Use
- Analyzing completed experiment results for ship decisions
- Checking on running experiment progress and early signals
- Understanding why an experiment succeeded or failed
- Investigating unexpected results or segment-level effects
Analysis Philosophy
Be comprehensive, not brief:
- Include specific numbers, percentages, and data points
- Explain statistical meaning AND business implications in plain language
- Cover all metrics (primary, secondary, guardrails) with actual values
- This is a single comprehensive analysis - do not rush or provide superficial summaries
Instructions
Step 0: Identify Experiment
If user provides a specific experiment:
- Accept experiment URL or experiment ID
- If URL: use
Amplitude:get_from_urlto extract details - If ID: proceed to Step 1
If user asks about experiments generally:
- Use
Amplitude:searchwithentityTypes: ["EXPERIMENT"]and relevant query terms - Present top 3-5 matches with names, IDs, and states
- Ask user which experiment to analyze
If no experiment specified:
- Ask explicitly for experiment URL, ID, or search terms and stop
Step 1: Retrieve and Validate Setup
Use Amplitude:get_experiments with experiment ID to capture:
- Experiment name, key, description, and state
- Start/end dates and duration
- Variants: names, traffic allocation
- Attached metrics: primary (recommendation=true), secondary, guardrails (stores as IDs)
- Bucketing strategy
Get metric names:
- Extract metric IDs from the experiment response (e.g., "c4pn8fkv")
- CRITICAL: Amplitude MCP cannot retrieve metric names by ID directly
- Workaround options:
- Search for experiment-related charts using
Amplitude:searchwithentityTypes: ["CHART"]and experiment name - Use
Amplitude:get_chartson related charts to examine their definitions for metric references - Check if experiment description contains links to metric documentation
- Search for experiment-related charts using
- If metric names cannot be found, report as descriptive placeholders:
- Primary metric: "Primary Goal Metric (ID: {id})"
- Secondary metrics: "Secondary Metric {index} (ID: {id})"
- Include metric IDs so users can look them up in Amplitude UI
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 528 lines · 49 tokens per session scan A af08e4f97467
analyze-experiments is a skill published in the GitHub repository amplitude/mcp-marketplace (35 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 5,038 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to analyze-experiment-consolidated, differing in 45 lines, and is treated as a copy.
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